FamEvent (version 1.3)

penci: Estimate the confidence intervals for the penetrances

Description

Estimates the simulation-based confidence intervals for the penetrances and the standard errors of the penetrance estimates at a given age, specific to gender and mutation status subgroups.

Usage

penci(est, cov, age=70, base.dist="Weibull", frailty.dist=NULL, agemin, n=1000)

Arguments

est

Parameter estimates of transformed baseline parameters (\(\lambda, \rho\)) and regression coefficients for gender and mutation status (\(\beta_s, \beta_g\)) for the assumed penetrance model.

cov

Variance-covariance matrix of the parameter estimates.

age

Specified age at which the penetrance is computed.

base.dist

Choice of baseline hazard distribution for the penetrance function. Possible choices are "Weibull", "loglogistic", "Gompertz", "lognormal", or "gamma". Default is "Weibull".

frailty.dist

Choice of frailty distribution, either "gamma" or "lognormal", if the penetrance function is based on a shared frailty model. Otherwise, frailty.dist = NULL is set as default.

agemin

Minimum age that the pentrance function starts.

n

Number of Monte-Carlo simulations for calculating standard errors and 95% confidence intervals for the penetrance estimate at a given age. Default value is n = 1000.

Value

Returns an object including the following values:

Estimate

Penetrance estimates (%) at the specified age, specific to gender and mutation status subgroups, based on the assumed penetrance model.

SE

Simulation-based standard errors of the penetrance estimates, specific to gender and mutation status subgroups.

lower

Simulation-based 2.5th percentile of the penetrance estimates, specific to gender and mutation status subgroups.

upper

Simulation-based 97.5th percentile of the penetrance estimates, specific to gender and mutation status subgroups.

Details

Calculations of standard errors of the penetrance estimates and 95% confidence intervals (CIs) for the penetrance at a given age are based on Monte-Carlo simulations of the estimated penetrance model.

A multivariate normal distribution is assumed for the parameter estimates, and \(n\) sets of the parameters are generated from the multivariate normal distribution with the parameter estimates and their variance and covariance matrix. For each simulated set, a penetrance estimate is calculated at a given age by substituting the simulated parameters into the penetrance function.

The standard error of the penetrance estimate at a given age is calculated by the standard deviation of penetrance estimates obtained from \(n\) simulations.

The 95% CI for the penetrance at a given age is calculated using the 2.5th and 97.5th percentiles of the penetrance estimates obtained from \(n\) simulations.

See Also

penmodelEM, penmodel, penplot, penf, print.penmodel,summary.penmodel,

print.summary.penmodel, plot.penmodel

Examples

# NOT RUN {
  
  # Family data simulated from population-based design using a Weibull baseline hazard 
  
  fam <- simfam(N.fam=300, design="pop+", variation="none", base.dist="Weibull", 
                base.parms=c(0.01,3), vbeta=c(-1.13, 2.35), agemin=20, allelefreq=0.02)
  
  # Penetrance model fit for simulated family data
  
  fit <- penmodel(parms=c(0.01, 3), vbeta=c(-1.13, 2.35), data=fam, 
                  design="pop+", base.dist="Weibull")
  
  # 95% confidence intervals for the penetrance at age 50 based on 1000 simulations
  
  penci(fit$coefficients, fit$varcov, age=50, base.dist="Weibull", agemin=20, n=1000)
  

# }